Gerhard Kienle Institute for Medical Theory, Integrative and Anthroposophic Medicine, Medical Department, Private University of Witten/Herdecke, Alfred-Herrhausenstr. 50, 58448, Witten, Germany.
Support Care Cancer. 2011 Aug;19(8):1197-209. doi: 10.1007/s00520-010-0939-1. Epub 2010 Jul 1.
Understanding how the information needs of cancer patients (CaPts) vary is important because met information needs affect health outcomes and CaPts' satisfaction. The goals of the study were to identify subgroups of CaPts based on self-reported cancer- and treatment-related information needs and to determine whether subgroups could be predicted on the basis of selected sociodemographic, clinical and clinician-patient relationship variables.
Three hundred twenty-three CaPts participated in a survey using the "Cancer Patients Information Needs" scale, which is a new tool for measuring cancer-related information needs. The number of information need subgroups and need profiles within each subgroup was identified using latent class analysis (LCA). Multinomial logistic regression was applied to predict class membership.
LCA identified a model of five subgroups exhibiting differences in type and extent of CaPts' unmet information needs: a subgroup with "no unmet needs" (31.4% of the sample), two subgroups with "high level of psychosocial unmet information needs" (27.0% and 12.0%), a subgroup with "high level of purely medical unmet information needs" (16.0%) and a subgroup with "high level of medical and psychosocial unmet information needs" (13.6%). An assessment of sociodemographic and clinical characteristics revealed that younger CaPts and CaPts' requiring psychological support seem to belong to subgroups with a higher level of unmet information needs. However, the most significant predictor for the subgroups with unmet information needs is a good clinician-patient relationship, i.e. subjective perception of high level of trust in and caring attention from nurses together with high degree of physician empathy seems to be predictive for inclusion in the subgroup with no unmet information needs.
The results of our study can be used by oncology nurses and physicians to increase their awareness of the complexity and heterogeneity of information needs among CaPts and of clinically significant subgroups of CaPts. Moreover, regression analyses indicate the following association: Nurses and physicians seem to be able to reduce CaPts' unmet information needs by establishing a relationship with the patient, which is trusting, caring and empathic.
了解癌症患者（CaPts）的信息需求如何变化很重要，因为满足信息需求会影响健康结果和 CaPts 的满意度。本研究的目的是根据自我报告的癌症和治疗相关信息需求确定 CaPts 的亚组，并确定是否可以根据选定的社会人口统计学、临床和医患关系变量预测亚组。
323 名 CaPts 参与了一项使用“癌症患者信息需求”量表的调查，该量表是一种用于测量癌症相关信息需求的新工具。使用潜在类别分析（LCA）确定亚组的数量和每个亚组内的需求特征。应用多项逻辑回归预测类别归属。
LCA 确定了一个由五个亚组组成的模型，这些亚组在 CaPts 未满足信息需求的类型和程度上存在差异：一个“无未满足需求”的亚组（样本的 31.4%），两个“高水平心理社会未满足信息需求”的亚组（27.0%和 12.0%），一个“高水平纯粹医学未满足信息需求”的亚组（16.0%）和一个“高水平医学和心理社会未满足信息需求”的亚组（13.6%）。对社会人口统计学和临床特征的评估表明，年轻的 CaPts 和需要心理支持的 CaPts 似乎属于未满足信息需求水平较高的亚组。然而，未满足信息需求亚组的最显著预测因素是良好的医患关系，即对护士高度信任和关怀以及医生高度同理心的主观感知，似乎是归入无未满足信息需求亚组的预测因素。
本研究结果可被肿瘤护士和医生用于提高他们对 CaPts 信息需求的复杂性和异质性以及 CaPts 临床重要亚组的认识。此外，回归分析表明以下关联：护士和医生似乎可以通过与患者建立信任、关怀和同理心的关系来减少 CaPts 的未满足信息需求。
